Hypoglycemia Prevention via Personalized Glucose-Insulin Models Identified in Free-Living Conditions
Hypoglycemia Prevention via Personalized Glucose-Insulin Models Identified in Free-Living Conditions
复制标题
通过在自由生活条件下识别的个性化葡萄糖胰岛素模型预防低血糖
DOI：
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发表时间：
2019
影响因子：
5
通讯作者：
L. Magni
中科院分区：
文献类型：
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作者：
C. Toffanin;E. M. Aiello;C. Cobelli;L. Magni
背景资料：本研究的目的是显示基于针对不同受试者的患者定制的葡萄糖-胰岛素模型（PTM）的个体化低血糖预测警报（IHPA）的有效性。患者间变异性要求在一个月的试验期间从自由生活条件下收集的数据中确定PTM。研究方法：一种新的脉冲响应（IR）识别技术已应用于自由生活数据，以识别能够预测未来血糖趋势并预防低血糖事件的PTM。脉冲反应已应用于阿姆斯特丹大学医学中心的7例1型糖尿病（T1 D）患者。由于PTM的良好预测能力，为每位患者设计了个性化的低血糖预测警报。结果如下：PTM性能的拟合指数（FIT），决定系数，皮尔逊相关系数与人口FIT为63.74%。在7名T1 D患者中评价了IHPA，目的是提前（45 - 10分钟）预测不可避免的低血糖事件;这些系统在灵敏度、精密度和准确度方面表现出比先前发表的结果更好的性能。结论：拟议的工作表明，成功的结果，获得了应用IR的一整套患者，参与者的一个月的试验。在低血糖预防方面对个体化低血糖预测警报进行了评估：使用PTM可以检测到一个月试验期间发生的84.67%的低血糖事件，平均误报率低于0.4%。PTM的有前途的预测能力可以成为新一代人工胰腺个性化模型预测控制的关键因素。
Background: The objective of this research is to show the effectiveness of individualized hypoglycemia predictive alerts (IHPAs) based on patient-tailored glucose-insulin models (PTMs) for different subjects. Interpatient variability calls for PTMs that have been identified from data collected in free-living conditions during a one-month trial. Methods: A new impulse-response (IR) identification technique has been applied to free-living data in order to identify PTMs that are able to predict the future glucose trends and prevent hypoglycemia events. Impulse response has been applied to seven patients with type 1 diabetes (T1D) of the University of Amsterdam Medical Centre. Individualized hypoglycemia predictive alert has been designed for each patient thanks to the good prediction capabilities of PTMs. Results: The PTMs performance is evaluated in terms of index of fitting (FIT), coefficient of determination, and Pearson’s correlation coefficient with a population FIT of 63.74%. The IHPAs are evaluated on seven patients with T1D with the aim of predicting in advance (between 45 and 10 minutes) the unavoidable hypoglycemia events; these systems show better performance in terms of sensitivity, precision, and accuracy with respect to previously published results. Conclusion: The proposed work shows the successful results obtained applying the IR to an entire set of patients, participants of a one-month trial. Individualized hypoglycemia predictive alerts are evaluated in terms of hypoglycemia prevention: the use of a PTM allows to detect 84.67% of the hypoglycemia events occurred during a one-month trial on average with less than 0.4% of false alarms. The promising prediction capabilities of PTMs can be a key ingredient for new generations of individualized model predictive control for artificial pancreas.
影响因子：
5.4
作者：
Buckingham, Bruce;Cobry, Erin;Chase, H. Peter
通讯作者：
Chase, H. Peter